Chargeback reduction planning for information technology management

ABSTRACT

Minimizing cost chargeback in an information technology (IT) computing environment including multiple resources. One implementation involves determining time-based usage patterns and allocation statistics for a plurality of resources and associated resource workloads. Using a regression function for determining a correlation of response time with resource usages and outstanding input/output instructions for the plurality of resources. Based on the time-based usage patterns, allocation statistics and the correlation, deriving an interpolation using positive and negative integrals to minimize a difference between allocated resource values and average allocation values. Determining service level objectives (SLOs) and resource allocation for minimizing cost chargeback for the resource workloads based on the derived interpolation.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation patent application of U.S.patent application Ser. No. 12/567,582, filed on Sep. 25, 2009, thedisclosure of which is incorporated herein its entirety by reference.

BACKGROUND

1. Field of the Invention

The present invention relates generally to an information technology(IT) environment, and more in particularly, to reducing chargeback costsin an IT environment.

2. Background Information

With increasing automation of business processes within enterprises, thedemand for information technology (IT) infrastructure is increasingexponentially, and is now a significant percentage of the totaloperating cost of a business. The capacity, performance, andavailability demands of individual departments within each enterpriseare being consolidated from isolated server-storage silos, to a unifiedvirtualized environment of servers hosting multiple on-demand virtualmachines, with transparent access to the entire storage subsystem usingstorage area networks (SANs). While such consolidation helps management,it poses challenges for Chief Information Officers (CIOs) responsiblefor containing IT costs and regulating usage of the infrastructurewithin departments. Chargeback is a process used to regulate IT costs bycharging each department proportionally according to the resourceallocated to it. This fosters efficient use of the available resourcesand also makes departments aware of their IT usage and associated costs.

A typical enterprise environment includes multiple departments, eachutilizing custom IT applications and IT resource services. With theadvancement of technologies such as virtualization and multi-corearchitectures, such IT custom applications and resource services aredeployed in a shared and consolidated server-storage environment,typically managed by the enterprise IT department. Resource allocationfor the applications is provided either by humans or resource planners.One example of resource allocation planner is TotalStorage ProductivityCenter (TPC) Storage Area Network (SAN) Planner. The allocationtechnique is dependent on the application Service Level Objectives(SLO), defined in terms of maximum latency, minimum throughput, etc. Theallocation technique may also depend on quality attributes including nosingle point of failure, disaster recovery support, etc. Capacityplanning involves utilizing automated tools to allocate a set ofresources for a given set of SLOs. This is accomplished in two broadsteps: first, the resources needed to achieve SLOs of each customer aredetermined using workload and device models (e.g., queuing theory model)and second, the resources are allocated from available resources usingone of the many multi-dimensional bin packing algorithms. An ITdepartment keeps track of the usage of these resources and depending ontheir usage allocates costs to each department in the form of achargeback.

Depending on the chargeback policies, departments may be charged,whether or not they use the resources allocated to them. Although an ITdepartment recovers total operating cost in the form of chargeback,enterprise as a whole may suffer due to the opportunity cost associatedwith the unused resources. System administrators or IT service providerswhile performing resource allocations attempt to achieve one or more ofthe following goals: satisfy customers SLOs, optimize the overallutilization of the resources, accommodate as many customers as possible,maximize profits and reduce operational costs. Because in general, ITcustomers and providers are conservative, and resources areover-provisioned to handle peak loads. This translates to misuse ofresources and higher chargeback for customers.

BRIEF SUMMARY

Reducing cost chargeback in an information technology (IT) computingenvironment including multiple resources is provided. An embodimentinvolves a system including an input module configured to input networkstatistics for a plurality of system resources and a plurality of costchargeback models. The system further includes an evaluation moduleconfigured to evaluate time-based resource usage based on the networkstatistics to result in at least one resource usage pattern. The systemalso includes a chargeback optimization module configured to determinecost reduction recommendations based on the at least one resource usagepattern and the plurality of cost chargeback models.

Another embodiment involves a process wherein resource usage andallocation statistics are stored for a multitude of resources andassociated cost policies. Then, time-based usage patterns are determinedfor the resources from the statistics. A correlation of response timewith resource usages and outstanding input/output transactions isdetermined. Based on usage patterns and the correlation, a multitude ofpotential cost reduction recommendations are determined. Further, amultitude of integrals are obtained based on the potential costreduction recommendations, and a statistical integral is obtained basedon the statistics. A difference between the statistical integral andeach of the multiple integrals is obtained and compared with a thresholdto determine potential final cost reduction recommendations. A finalcost reduction recommendation is then selected from the potential costreduction recommendations.

Yet another embodiment involves a computer program product for reducingcost chargeback in an IT computing environment including multipleresources. The computer program product comprises a computer usablemedium including a computer readable program having programinstructions. The computer readable program when executed on a computercauses the computer to perform the above process.

Other aspects and advantages of the present invention will becomeapparent from the following detailed description, which, when taken inconjunction with the drawings, illustrate by way of example theprinciples of the invention.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

For a fuller understanding of the nature and advantages of theinvention, as well as a preferred mode of use, reference should be madeto the following detailed description read in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates a system for reducing cost chargeback in an ITcomputing environment including multiple resources, according to anembodiment of the invention;

FIG. 2 illustrates an enterprise network including a system for reducingcost chargeback in an IT computing environment including multipleresources according to an embodiment of the invention;

FIG. 3 illustrates inputs and outputs of a system for reducing costchargeback in an IT computing environment including multiple resources,according to an embodiment of the invention;

FIG. 4 illustrates a graph showing positive and negative integrals usedfor optimizing chargeback reduction;

FIG. 5 illustrates a process for reducing cost chargeback in an ITcomputing environment including multiple resources, according to anembodiment of the invention; and

FIG. 6 illustrates a distributed system, according to an embodiment ofthe invention.

DETAILED DESCRIPTION

The following description is made for the purpose of illustrating thegeneral principles of the invention and is not meant to limit theinventive concepts claimed herein. Further, particular featuresdescribed herein can be used in combination with other describedfeatures in each of the various possible combinations and permutations.Unless otherwise specifically defined herein, all terms are to be giventheir broadest possible interpretation including meanings implied fromthe specification as well as meanings understood by those skilled in theart and/or as defined in dictionaries, treatises, etc.

The description may disclose several preferred embodiments for reducingcost chargeback in an information technology (IT) computing environmentincluding multiple resources, as well as operation and/or componentparts thereof. While the following description will be described interms of chargeback optimization for clarity and to place the inventionin context, it should be kept in mind that the teachings herein may havebroad application to all types of systems, devices and applications.

Reducing cost chargeback in an information technology (IT) computingenvironment including multiple resources, is provided. One embodimentcomprises a system for reducing cost chargeback in an informationtechnology (IT) computing environment, the system including an inputmodule configured to input network statistics for a plurality of systemresources and a plurality of cost chargeback models. The system furtherincludes an evaluation module configured to evaluate time-based resourceusage based on the network statistics to result in at least one resourceusage pattern. The system also includes a chargeback optimization moduleconfigured to determine cost reduction recommendations based on the atleast one resource usage pattern and the plurality of cost chargebackmodels.

The system implements a process wherein resource usage and allocationstatistics are stored for a multitude of resources and associated costpolicies. Then, time-based usage patterns are determined for theresources from the network statistics. A correlation of response timewith resource usages and outstanding input/output transactions isdetermined. Based on usage patterns and the correlation, a multitude ofpotential cost reduction recommendations are determined. Further, amultitude of integrals are obtained based on the potential costreduction recommendations, and a statistical integral is obtained basedon the statistics. A difference between the statistical integral andeach of the multiple integrals is obtained and compared with a thresholdto determine potential final cost reduction recommendations. A finalcost reduction recommendation is then selected from the potential costreduction recommendations.

FIG. 1 illustrates an example block diagram of a system for reducingcost chargeback in an information technology (IT) computing environmentincluding multiple resources according to one embodiment of theinvention. The system 100 includes an input module 110, an evaluationmodule 120 and a chargeback optimization module 130.

FIG. 2 illustrates system 100 as part of an enterprise system 200including server devices 1-N 210, switches 1-N 215, storage units 1-N220 that include physical memory 221 and logical memory 222, and clientdevices 1-N 230. The system 100 functions as a cost-reduction plannerfor enterprise systems.

The system 100 analyzes the historic pattern of resource usage bydifferent applications and recommends new allocation strategies thatreduce the discrepancies between the actual usage and allocation.Time-varying SLOs define trends and seasonality of the resource workload(resource load), allowing adjustments to the allocation based on theapplication requirements, for reducing chargeback. The system 100provides the ability to make changes in allocation and SLOs that allowmeeting budget requirements of IT departments, and performing what-ifanalysis in evaluating cost savings for different SLOs and provisioninglevels.

FIG. 3 illustrates a process 300 for inputs and outputs of the system100 in reducing cost chargeback in an IT computing environment includingmultiple resources according to an embodiment of the invention. Theinput module 110 inputs resource usage history data, Service LevelObjectives (SLO), allocation data and resource configuration data.Specifically, in processing block 310, the input module 110 obtainsinformation from Storage Resource Management (SRM) tools, includinginformation about average resource usage, SLO, capacity, allocatedresources, and configuration such as cost policy (for heterogeneousresources). The evaluation module 320 then analyzes the inputinformation and provides strategies to reduce application chargeback,including: changing current allocation values, time-varying SLO andrecommending a new allocation or SLOs.

In processing block 320 the evaluation module 120 evaluates time-serieschargeback models 340 for performance usage data and utilizes aregression function to correlate response time with resource load andnumber of outstanding IOs. An interpolation is derived using white-boxtechniques or by applying known machine learning algorithms such as CARTand M5. The evaluation module 120 uses positive-negative integralfunctions to optimize resource allocation that converges to the averageapplication throughput. These integrals are defined as follows:

Positive Integral (A+): Area of curve above allocated value.

Negative Integral (A−): Area of curve in between average and allocatedvalue.

The goal is to minimize the difference between allocated and averagevalues, such as illustrated by relation (1) below:

$\begin{matrix}\left. {{{Min}{\int_{0}^{t}A}} -}\rightarrow{{Allocated} \approx {Average}} \right. & (1)\end{matrix}$

The chargeback optimization module 130 achieves optimization using anobjective function such as illustrated by relation (2) below:

$\begin{matrix}{{{Max}{\sum\limits_{j = 0}^{devices}{\sum\limits_{i = 0}^{workloads}{A_{ij}^{+} \times C_{j} \times {SLO}_{i}}}}} - {\sum\limits_{j = 0}^{devices}{\sum\limits_{i = 0}^{workloads}{A_{ij}^{-} \times C_{j} \times {SLO}_{i}}}}} & (2)\end{matrix}$

-   -   wherein    -   A_(ij)=Integral area of resource workload i at device j,    -   C_(j)=Cost rate for device j,    -   SLO_(i)=Operation zone of resource workload i.    -   The constraints on the above objective function are:    -   latency_(i)=SLO(resource workload),    -   latency_(i)=f(OutstandingIOS), and    -   OutstandingIOS=g(Load, A_(ij)).

FIG. 4 shows an example graph 400 illustrating positive and negativeintegrals used for optimizing chargeback reduction. The vertical axisrepresents the amount of resource used and the horizontal axisrepresents the time. The objective function determines SLOs andallocation that substantially optimizes (i.e., minimizes) chargeback forthe resource workloads under consideration. The objective functionutilizes non-linear optimization that interpolates the impact ofallocation change on the application latency in relation to the SLO. Theinterpolation uses regression functions that quantify resource workloadlatency as a function of number of outstanding IOs.

Finally in the processing block 330 (FIG. 3), the system 100 providesrecommendations including: new allocations, new SLOs, new schedule andtime varying SLOs. The goal of these recommendations is to minimize thegap between the actual usage and the allocated resources, therebyreducing chargeback. The ‘new allocation’ strategy according to anembodiment of the invention recommends a change in the amount ofresource allocation or a change in the type of resource allocated. Forexample, moving a workload from a very high-end server to a low-endserver may satisfy its requirements as well as reduce chargeback. The‘new SLOs’ strategy according to an embodiment of the inventionrecommend a change in the workload SLO. This is useful in scenarioswhere SLOs are incorrectly set to levels that are never attained andresults in waste of resource and higher chargeback.

The ‘time varying SLOs’ strategy according to an embodiment of theinvention recommends different allocation at different time of the dayor at different month of the year. Typically, SLOs are defined such thatthey can handle all the peaks in the workloads. This results inover-provisioning of resources. Most workloads, however, consumedifferent amount of resources at different point of operations. Forexample, some workloads may consume more resources during the day. Otherworkloads may consume more resources during holiday seasons, etc. Thetime-varying SLOs strategy according to an embodiment of the inventionadapts the resource allocation according to the time varying nature ofresource usage. The ‘new schedule’ strategy according to an embodimentof the invention defers workload processing to non-peak hours when thedemand of IT resources is lower. For example, ‘backup’ jobs in adatacenter can be executed at night when the resource utilization istypically low.

FIG. 5 shows a more detailed process 500 for reducing cost chargeback bythe system 100, according to an embodiment of the invention. In processblock 510, resource usage and allocation statistics are gathered fromresource management tools such as TotalStrorage Productivity Center(TPC) and Tivoli Application Dependency Discovery Manager (TADDM). Inprocess block 520, time-series (time-based) usage patterns are evaluatedfor the plurality of resources from the statistics (e.g., calculatingaverage, peak, median throughput). In process block 530, a regressionfunction is generated to correlate response time with resource usagesand outstanding input/output instructions for the plurality ofresources. In one implementation, response time is correlated withresource workload and number of outstanding input/output operations(IOs). In process block 540, based on usage patterns and thecorrelation, a plurality of potential cost reduction recommendations isobtained. In one implementation, multi-strategy optimization isperformed using randomized bin-packing.

Sub-processes of block 540 involve blocks 550 and 560, wherein in block550, a plurality of integrals based on the plurality of potential costreduction recommendations is determined, and a statistical integralbased on the statistics is determined. In one implementation, positiveand negative integrals are calculated in relation to averaged andallocated throughput.

The positive integrals may have values above a current resourceallocation, and the negative integrals may have a value between anaverage resource allocation value and the current resource allocationvalue.

In block 560, the allocations are modified using successive constraintrelaxation. Then, a difference between the statistical integral and eachof the plurality of integrals is compared with a threshold to determinepotential final cost reduction recommendations. Specifically, in processblock 570 it is determined if the variance between the integrals isgreater than a threshold. If not, then in block 580 results arereturned. These results include one or more strategies (discussed in theprevious paragraph.) Otherwise, in block 590 the historical time seriesusage window is divided into smaller intervals and recursively theprocess is repeated. Accordingly, a final cost reduction strategy(recommendation) is selected from the potential cost reductionstrategies.

The final cost recommendation may comprise a time varying service levelobjective. The final cost recommendation may comprise one of a change ofa current resource allocation value and a new resource allocation. Thefinal cost reduction recommendation may comprise a new schedule forworkload execution, or may define resource allocation that minimizeschargeback costs.

FIG. 6 illustrates a distributed system 600 according to one embodimentof the invention, comprising a distributed network including a pluralityof distributed enterprise centers 610 1-N and the chargebackoptimization system 100. In this embodiment, the distributed enterprisecenters 610 each use the chargeback optimization system 100 for reducingcost chargeback as described herein.

The embodiments of the invention can take the form of an entirelyhardware embodiment, an entirely software embodiment or an embodimentcontaining both hardware and software elements. In a preferredembodiment, the invention is implemented in software, which includes butis not limited to firmware, resident software, microcode, etc.

Furthermore, the embodiments of the invention can take the form of acomputer program product accessible from a computer-usable orcomputer-readable medium providing program code for use by or inconnection with a computer, processing device, or any instructionexecution system. For the purposes of this description, acomputer-usable or computer readable medium can be any apparatus thatcan contain, store, communicate, or transport the program for use by orin connection with the instruction execution system, apparatus, ordevice.

The medium can be electronic, magnetic, optical, or a semiconductorsystem (or apparatus or device). Examples of a computer-readable mediuminclude, but are not limited to, a semiconductor or solid state memory,magnetic tape, a removable computer diskette, a RAM, a read-only memory(ROM), a rigid magnetic disk, an optical disk, etc. Current examples ofoptical disks include compact disk-read only memory (CD-ROM), compactdisk-read/write (CD-R/W) and DVD.

I/O devices (including but not limited to keyboards, displays, pointingdevices, etc.) can be connected to the system either directly or throughintervening controllers. Network adapters may also be connected to thesystem to enable the data processing system to become connected to otherdata processing systems or remote printers or storage devices throughintervening private or public networks. Modems, cable modem and Ethernetcards are just a few of the currently available types of networkadapters.

In the description above, numerous specific details are set forth.However, it is understood that embodiments of the invention may bepracticed without these specific details. For example, well-knownequivalent components and elements may be substituted in place of thosedescribed herein, and similarly, well-known equivalent techniques may besubstituted in place of the particular techniques disclosed. In otherinstances, well-known structures and techniques have not been shown indetail to avoid obscuring the understanding of this description.

Reference in the specification to “an embodiment,” “one embodiment,”“some embodiments,” or “other embodiments” means that a particularfeature, structure, or characteristic described in connection with theembodiments is included in at least some embodiments, but notnecessarily all embodiments. The various appearances of “an embodiment,”“one embodiment,” or “some embodiments” are not necessarily allreferring to the same embodiments. If the specification states acomponent, feature, structure, or characteristic “may”, “might”, or“could” be included, that particular component, feature, structure, orcharacteristic is not required to be included. If the specification orclaim refers to “a” or “an” element, that does not mean there is onlyone of the element. If the specification or claims refer to “anadditional” element, that does not preclude there being more than one ofthe additional element.

While certain exemplary embodiments have been described and shown in theaccompanying drawings, it is to be understood that such embodiments aremerely illustrative of and not restrictive on the broad invention, andthat this invention not be limited to the specific constructions andarrangements shown and described, since various other modifications mayoccur to those ordinarily skilled in the art.

1. A method of minimizing cost chargeback in an information technologycomputing environment including multiple resources, comprising:determining time-based usage patterns and allocation statistics for aplurality of resources and associated resource workloads; using aregression function for determining a correlation of response time withresource usages and outstanding input/output instructions for theplurality of resources; based on the time-based usage patterns,allocation statistics and the correlation, deriving an interpolationusing positive and negative integrals to minimize a difference betweenallocated resource values and average allocation values; and determiningservice level objectives (SLOs) and resource allocation for minimizingcost chargeback for the resource workloads based on the derivedinterpolation.
 2. The method of claim 1, further comprising providing arecommendation for resource allocation that minimizes chargeback costs.3. The method of claim 1, wherein the interpolation comprises anobjective function based on an integral area of the resource workload,cost rate for each device, and operation zone of each resource workload.4. The method of claim 3, wherein the positive integrals comprise anarea of a curve above allocated resource values, and the negativeintegrals comprise an area of a curve between average allocated valuesand allocated resource values.
 5. The method of claim 3, wherein theobjective function comprises: $\begin{matrix}{{{Max}{\sum\limits_{j = 0}^{devices}{\sum\limits_{i = 0}^{workloads}{A_{ij}^{+} \times C_{j} \times {SLO}_{i}}}}} - {\sum\limits_{j = 0}^{devices}{\sum\limits_{i = 0}^{workloads}{A_{ij}^{-} \times C_{j} \times {SLO}_{i}}}}} & (2)\end{matrix}$ wherein A_(ij)=integral area of resource workload i atdevice j, where (A+): area of curve above allocated resource value, and(A−): area of curve in between average allocated value and allocatedresource value, C_(j)=Cost rate for device j, and SLO_(i)=Operation zoneof resource workload i.
 6. The method of claim 5, wherein constraintsfor the objective function comprise latency of service level objectivesfor each resource workload, latency based on outstanding input/outputoperations and load of outstanding input/output operations.
 7. Themethod of claim 3, wherein the objective function uses non-linearoptimization that interpolates impact of resource allocation change onapplication latency in relation to SLO.
 8. The method of claim 1,wherein the regression function quantifies resource workload latency asa function of number of outstanding input/output operations.
 9. Themethod of claim 1, wherein the SLOs use randomized bin packing
 10. Asystem comprising: an evaluation module that evaluate time-basedresource usage patterns and allocation statistics for a plurality ofresources and associated resource workloads; and a chargebackoptimization module that determines a correlation of response time withresource usages and outstanding input/output instructions for theplurality of resources, and based on the time-based resource usagepatterns, allocation statistics and the correlation, derives aninterpolation using positive and negative integrals to minimize adifference between allocated resource values and average allocationvalues, and determines service level objectives (SLOs) and resourceallocation for minimizing cost chargeback for the resource workloadsbased on the derived interpolation.
 11. The system of claim 10, furthercomprising an enterprise network coupled to the system.
 12. The systemof claim 10, wherein the plurality of resources comprise: storagedevices; latency allocation; at least one server device; a plurality ofswitches; and a plurality of applications executed by the at least oneserver device.
 13. The system of claim 10, wherein a final costreduction recommendation is provided by the chargeback optimizationmodule, wherein the final cost reduction recommendation comprises a timevarying SLO.
 14. The system of claim 13, wherein the final costreduction recommendation comprises one of a change of a current resourceallocation value and a new resource allocation.
 15. The system of claim13, wherein the final cost reduction recommendation defines resourceallocation that minimizes chargeback costs.
 16. A computer programproduct for minimizing chargeback costs comprising a non-transitorycomputer usable medium including a computer readable program, whereinthe computer readable program when executed on a computer causes thecomputer to: determine time-based usage patterns and allocationstatistics for a plurality of resources and associated resourceworkloads; use a regression function for determining a correlation ofresponse time with resource usages and outstanding input/outputinstructions for the plurality of resources; based on the time-basedusage patterns, allocation statistics and the correlation, derive aninterpolation using positive and negative integrals to minimize adifference between allocated resource values and average allocationvalues; and determine service level objectives (SLOs) and resourceallocation for minimizing cost chargeback for the resource workloadsbased on the derived interpolation.
 17. The computer program product ofclaim 16, wherein the computer readable program when executed on thecomputer further causes the computer to: provide a recommendation forresource allocation that minimizes chargeback costs.
 18. The computerprogram product of claim 16, wherein the interpolation comprises anobjective function based on an integral area of the resource workload,cost rate for each device, and operation zone of each resource workload.19. The computer program product of claim 18, wherein the positiveintegrals comprise an area of a curve above allocated resource values,and the negative integrals comprise an area of a curve between averageallocated values and allocated resource values.
 20. The computer programproduct of claim 16, wherein the objective function uses non-linearoptimization that interpolates impact of resource allocation change onapplication latency in relation to SLOs.